A digital watermark is an invisible identifier embedded into image data, video data, or audio data, and can be used for copyright protection, authentication, and tracking of multi-media data, and the like.
Digital watermarking technologies may be divided into express watermarking and blind watermarking according to watermark extraction. Extraction of an express watermark requires original data in which the watermark is embedded, while a blind watermark only requires a key. Generally speaking, the express watermark is robust, but cost a lot in storage, and does not meet a practical requirement very well. Therefore, the blind watermark is a trend in watermarking algorithm research.
Image watermarking technologies may be divided into a globally embedded watermarking and a locally embedded watermarking according to the way in which a watermark is embedded. The watermark information in the globally embedded watermarking is embedded into a transformation domain of the whole image, such as a space domain, a frequency domain, a wavelet domain, or the like. Experiments show that such a method has a relatively robust resistance against an interference such as JPEG compression, interference by noise, filtering, or the like. However, as the information is embedded in the whole image, when the image is tailored, the size of the original image and the position of the tailored image in the original image are unavailable, it will be difficult to determine the position where the watermark is embedded, and therefore a globally embedded watermark is susceptible to an attack by tailoring; a locally embedded watermark is embedded based on content of the image, and the watermark information is repeatedly embedded onto a transformation domain of the vicinity of a relatively stable and prominent feature point in the image. Thus, even if image goes through a large-scale tailoring or modification, the position of the watermark can still be determined through the feature point, thereby recovering the watermark information. As in theory, the localized watermark has a good robustness against various attacks, and the localized watermark has become a hot spot of research in recent years.
Localized blind watermarking is a combination of both the blind watermarking and the localized watermarking. Localized blind watermarking in relevant art mainly relates to techniques such as watermark embedding, watermark detecting and extracting, and the like. A flow of embedding a localized blind watermark is as follows: first, a number of feature points in the original image is extracted, and local DCT (Discrete Cosine Transform) or Wavelet transformation is performed on the vicinity of the feature points; then, a pseudorandom bit sequence generated by a specific key (namely, the watermark information) is embedded into the transformation domain according to a preset embedding rule; finally, a local inverse transformation is performed to obtain a watermarked image. A flow of detecting and extracting a localized blind watermark is as follows: a feature point of an image is extracted, and local DCT or Wavelet transformation is performed on the vicinity of the feature point; then, a bit sequence with the same dimension as the watermark information is extracted based on a preset extracting rule, and a match is performed to obtain a degree of similarity of the bit sequence with a pre-embedded watermark information (available from the key); and a watermark is deemed to exist if the degree of similarity is greater than a certain threshold T, otherwise no watermark is deemed to exist.
There are some disadvantages associated with the aforementioned localized blind watermarking, namely:
1) Watermark detection is still key-dependent, and during the detection, different keys are required for the match in order to complete the detection, thus leading to a low detecting efficiency;
2) when performing the match to obtain a degree of similarity with the watermark information, each bit in a watermark information space has, by default, the same credibility in the match, leading to no accurate match in some cases, for example: assume an unknown sequence of 11111, watermark A of 11110, watermark B of 01111, in which case it is impossible to determine which one of watermarks A and B is more credible when the same extracted sequence has the same degree of similarity with the two different watermarks A and B; according to a match based on the difference, there is only one bit in both watermarks A and B that is different from that in the unknown sequence, and it is impossible to determine whether the unknown sequence is watermark A or watermark B, thus leading to a low detecting accuracy.